Papers Relation Classification

Bidirectional Long Short-Term Memory Networks for Relation Classification

Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Paths

Comparing Word Representations for Implicit Discourse Relation Classification

Temporal Relation Classification using a Model of Tense and Aspect

A VSM-based Statistical Model for the Semantic Relation Interpretation of Noun-Modifier Pairs

Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Path

Relation classification is an important research arena in the field of natural language processing (NLP). In this paper, we present SDP-LSTM, a novel neural network to classify the relation of two entities in a sentence.…

Relation Classification via Recurrent Neural Network

Deep learning has gained much success in sentence-level relation classification. For example, convolutional neural networks (CNN) have delivered competitive performance without much effort on feature engineering as the c…

A Dependency-Based Neural Network for Relation Classification

Previous research on relation classification has verified the effectiveness of using dependency shortest paths or subtrees. In this paper, we further explore how to make full use of the combination of these dependency in…

MultiGranCNN: An Architecture for General Matching of Text Chunks on Multiple Levels of Granularity

Spectral Semi-Supervised Discourse Relation Classification

Shallow Discourse Parsing with Syntactic and (a Few) Semantic Features

The DCU Discourse Parser for Connective, Argument Identification and Explicit Sense Classification

Semantic Relation Classification via Convolutional Neural Networks with Simple Negative Sampling

Syntactic features play an essential role in identifying relationship in a sentence. Previous neural network models often suffer from irrelevant information introduced when subjects and objects are in a long distance. In…

BluLab: Temporal Information Extraction for the 2015 Clinical TempEval Challenge

SpRL-CWW: Spatial Relation Classification with Independent Multi-class Models

Relation Extraction: Perspective from Convolutional Neural Networks

Improved Relation Extraction with Feature-Rich Compositional Embedding Models

Compositional embedding models build a representation (or embedding) for a linguistic structure based on its component word embeddings. We propose a Feature-rich Compositional Embedding Model (FCM) for relation extractio…

Embedding Semantic Relations into Word Representations

Learning representations for semantic relations is important for various tasks such as analogy detection, relational search, and relation classification. Although there have been several proposals for learning representa…

Improving the Inference of Implicit Discourse Relations via Classifying Explicit Discourse Connectives

Chain Based RNN for Relation Classification